As shown in Figure REF , we build our network with several PVC layers sequentially. Grid resolution decreases while output feature channel increases from shallow to deep. Different PVC layers extract different levels of semantic information. We add supervision on each PVC layer output to enforce different levels of semantic feature learning. Similar strategy is adopted by [1]} for multi-scale medical image segmentation. In detail, we concatenate outputs from all PVC layers and use MLPs to produce a compact feature. Serving as a global guidance, this feature map is concatenated with the output of each PVC layer, then pass through a channel attention module to enhance feature representation of specific semantics. This attention module, inspired by [2]}, aggregates weighted features of all the channels into the original features, and models discriminability between channels. The output from channel attention module produce an auxiliary loss. We add up all the losses and average the prediction probabilities for the final prediction. We show that incorporating channel attention module in our network boosts performances and not necessarily slow down inference speed(see Section 4.4).
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